{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/quantum-algorithms-for-compositional-natural","title":"Quantum Algorithms for Compositional Natural Language Processing","arxiv_id":"1608.01406","date":"2016-08-04","proceeding":null,"authors":["William Zeng","Bob Coecke"],"abstract":"We propose a new application of quantum computing to the field of natural\nlanguage processing. Ongoing work in this field attempts to incorporate\ngrammatical structure into algorithms that compute meaning. In (Coecke,\nSadrzadeh and Clark, 2010), the authors introduce such a model (the CSC model)\nbased on tensor product composition. While this algorithm has many advantages,\nits implementation is hampered by the large classical computational resources\nthat it requires. In this work we show how computational shortcomings of the\nCSC approach could be resolved using quantum computation (possibly in addition\nto existing techniques for dimension reduction). We address the value of\nquantum RAM (Giovannetti,2008) for this model and extend an algorithm from\nWiebe, Braun and Lloyd (2012) into a quantum algorithm to categorize sentences\nin CSC. Our new algorithm demonstrates a quadratic speedup over classical\nmethods under certain conditions.","url_abs":"http://arxiv.org/abs/1608.01406v1","url_pdf":"http://arxiv.org/pdf/1608.01406v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"quantum-algorithms-for-compositional-natural","repo_url":"https://github.com/ICHEC/QNLP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1608.01406","atlas_url":"https://app.syntology.ai/?focus=1608.01406","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}